In this paper we introduce ISALux, a novel transformer-based approach for Low-Light Image Enhancement (LLIE) that seamlessly integrates illumination and semantic priors. Our architecture includes an original self-attention block, Hybrid Illumination and Semantics-Aware Multi-Headed Self-Attention (HISA-MSA), which integrates illumination and semantic segmentation maps for enhanced feature extraction. ISALux employs two self-attention modules to independently process illumination and semantic features, selectively enriching each other to regulate luminance and highlight structural variations in real-world scenarios. A Mixture of Experts (MoE)-based Feed-Forward Network (FFN) enhances contextual learning, with a gating mechanism conditionally activating the top K experts for specialized processing. To address overfitting in LLIE methods caused by distinct light patterns in benchmarking datasets, we enhance the HISA-MSA module with low-rank matrix adaptations (LoRA). Extensive qualitative and quantitative evaluations across multiple specialized datasets demonstrate that ISALux is competitive with state-of-the-art (SOTA) methods. Additionally, an ablation study highlights the contribution of each component in the proposed model.

Image dehazing, a crucial task in low-level vision, supports numerous practical applications, such as autonomous driving, remote sensing, and surveillance. This paper proposes IHDCP, a novel Inverted Haze Density Correction Prior for efficient single image dehazing. It is observed that the medium transmission can be effectively modeled from the inverted haze density map using correction functions with various gamma coefficients. Based on this observation, a pixel-wise gamma correction coefficient is introduced to formulate the transmission as a function of the inverted haze density map. To estimate the transmission, IHDCP is first incorporated into the classic atmospheric scattering model (ASM), leading to a transcendental equation that is subsequently simplified to a quadratic form with a single unknown parameter using the Taylor expansion. Then, boundary constraints are designed to estimate this model parameter, and the gamma correction coefficient map is derived via the Vieta theorem. Finally, the haze-free result is recovered through ASM inversion. Experimental results on diverse synthetic and real-world datasets verify that our algorithm not only provides visually appealing dehazing performance with high computational efficiency, but also outperforms several state-of-the-art dehazing approaches in both subjective and objective evaluations. Moreover, our IHDCP generalizes well to various types of degraded scenes. Our code is available at https://github.com/TaoLi-TL/IHDCP.

This letter introduces LYT-Net, a novel lightweight transformer-based model for low-light image enhancement. LYT-Net consists of several layers and detachable blocks, including our novel blocks-Channel-Wise Denoiser (CWD) and Multi-Stage Squeeze & Excite Fusion (MSEF)-along with the traditional Transformer block, Multi-Headed Self-Attention (MHSA). In our method we adopt a dual-path approach, treating chrominance channels$ U$and$ V$and luminance channel$ Y$as separate entities to help the model better handle illumination adjustment and corruption restoration. Our comprehensive evaluation on established LLIE datasets demonstrates that, despite its low complexity, our model outperforms recent LLIE methods. The source code and pre-trained models are available athttps://github.com/albrateanu/LYT-Net

We introduce a simple but robust method to restore the visibility of hazy images. Our non deep-learning strategy refines a simplistic approximation of the airlight by taking advantage of the O-HAZE dataset that contains also the corresponding haze-free images. Knowing that the transmission is generally characterized by small values in hazy scenes, based on the optical model, we first assumes zero transmission, and approximate the airlight by the input hazy image. Then, using hazy and corresponding haze-free images available from the O-HAZE dataset, a second airlight estimate can be computed by solving the optical model assuming a simplified transmission map derived from dark channel prior. Observing that the difference between these two airlight estimates, primarily contains the low frequencies of the hazy image, we refine the airlight approximation derived from a zero transmission by reinforcing its low frequency component. We extensively tested our approach on real world hazy images. The qualitative and quantitative evaluations demonstrate that our approach yields better results than previous physically-based image dehazing techniques, and favorably compares with the deep learning dehazing approaches.

Abstract Multi-exposure image fusion has emerged as an increasingly important and interesting research topic in information fusion. It aims at producing an image with high quality by fusing a set of differently exposed images. In this article, we present a pixel-level method for multi-exposure image fusion based on an information-theoretic approach. In our scheme, an information channel between two source images is used to compute the Rényi entropy associated with each pixel in one image with respect to the other image and hence to produce the weight maps for the source images. Since direct weight-averaging of the source images introduce unpleasing artifacts, we employ Laplacian multi-scale fusion. Based on this pyramid scheme, images at every scale are fused by weight maps, and a final fused image is inversely reconstructed. Multi-exposure image fusion with the proposed method is easy to construct and implement and can deliver, in less than a second for a set of three input images of size 512$\times $340, competitive and compelling results versus state-of-art methods through visual comparison and objective evaluation.

We introduce an effective fusion-based technique to enhance both day-time and night-time hazy scenes. When inverting the Koschmieder light transmission model, and by contrast with the common implementation of the popular dark-channel DehazeHeCVPR2009, we estimate the airlight on image patches and not on the entire image. Local airlight estimation is adopted because, under night-time conditions, the lighting generally arises from multiple localized artificial sources, and is thus intrinsically non-uniform. Selecting the sizes of the patches is, however, non-trivial. Small patches are desirable to achieve fine spatial adaptation to the atmospheric light, but large patches help improve the airlight estimation accuracy by increasing the possibility of capturing pixels with airlight appearance (due to severe haze). For this reason, multiple patch sizes are considered to generate several images, that are then merged together. The discrete Laplacian of the original image is provided as an additional input to the fusion process to reduce the glowing effect and to emphasize the finest image details. Similarly, for day-time scenes we apply the same principle but use a larger patch size. For each input, a set of weight maps are derived so as to assign higher weights to regions of high contrast, high saliency and small saturation. Finally the derived inputs and the normalized weight maps are blended in a multi-scale fashion using a Laplacian pyramid decomposition. Extensive experimental results demonstrate the effectiveness of our approach as compared with recent techniques, both in terms of computational efficiency and the quality of the outputs.

This article introduces a novel solution to improve image enhancement in terms of color appearance. Our approach, called Color Channel Compensation (3C), overcomes artifacts resulting from the severely non-uniform color spectrum distribution encountered in images captured under hazy night-time conditions, underwater, or under non-uniform artificial illumination. Our solution is founded on the observation that, under such adverse conditions, the information contained in at least one color channel is close to completely lost, making the traditional enhancing techniques subject to noise and color shifting. In those cases, our pre-processing method proposes to reconstruct the lost channel based on the opponent color channel. Our algorithm subtracts a local mean from each opponent color pixel. Thereby, it partly recovers the lost color from the two colors (red-green or blue-yellow) involved in the opponent color channel. The proposed approach, whilst simple, is shown to consistently improve the outcome of conventional restoration methods. To prove the utility of our 3C operator, we provide an extensive qualitative and quantitative evaluation for white balancing, image dehazing, and underwater enhancement applications.

In this paper we introduce a novel non-linear mapping technique to effectively decolorize images. Designed in a multi-scale fusion fashion, we first derive three input images represented by the color channels R, G and B. In order to transfer to the decolorized image only the relevant features of the derived inputs, we define two weight maps based on information theoretic approaches. The first weight map extracts visually salient regions based on a information maximization strategy while the second weight map filters the amount of local variation of each derived input computing local entropy per patch. Finally, to reduce the local distortions that might be introduced by the weight maps discontinuities, our decolorization strategy is designed in a multi-scale fusion. We also introduce a blind measure to accurately evaluate image decolorization methods. Our comprehensive qualitative and quantitative validation demonstrates that our method yields very competitive results.

Single image dehazing is an ill-posed problem that has recently drawn important attention. Despite the significant increase in interest shown for dehazing over the past few years, the validation of the dehazing methods remains largely unsatisfactory, due to the lack of pairs of real hazy and corresponding haze-free reference images. To address this limitation, we introduce Dense-Haze - a novel dehazing dataset. Characterized by dense and homogeneous hazy scenes, Dense-Haze contains 33 pairs of real hazy and corresponding haze-free images of various outdoor scenes. The hazy scenes have been recorded by introducing real haze, generated by professional haze machines. The hazy and haze-free corresponding scenes contain the same visual content captured under the same illumination parameters. Dense-Haze dataset aims to push significantly the state-of-the-art in single-image dehazing by promoting robust methods for real and various hazy scenes. We also provide a comprehensive qualitative and quantitative evaluation of state-of-the-art single image dehazing techniques based on the Dense-Haze dataset. Not surprisingly, our study reveals that the existing dehazing techniques perform poorly for dense homogeneous hazy scenes and that there is still much room for improvement.

In this letter we introduce a simple but effective concept, Color Channel Transfer (CCT), that is able to substantially improve the performance of various dehazing techniques. CCT is motivated by a key observation: in scattering media the information from at least one color channel presents high attenuation. To compensate for the loss of information in one color channel, CCT employs a color-transfer strategy and operates in a color opponent space that helps to compensate automatically the chromatic loss. The reference is computed by combining the details and saliency of the initial image with uniform gray image that assures a balanced chromatic distribution. The extensive qualitative and quantitative experiments demonstrate the utility of CCT as a preprocessing step for various dehazing problems such as day-time dehazing, night-time dehazing, and underwater image dehazing.

This paper introduces an effective strategy to enhance the visibility of hazy images, especially those obtained in night-time conditions. Compared to day-time, in night-time scenes, the lighting generally arises from multiple artificial sources and therefore may be considered intrinsically as being non-uniform. As a result, conventional global atmospheric light (airlight) estimation strategies become irrelevant. In this work, we propose a simple yet effective patch-based atmospheric light estimation. To circumvent the problem of selecting an appropriate patch size, we propose to estimate the atmospheric light on several patch sizes, and to define the local airlight as the average of those estimates. An extensive experimental validation demonstrates that the proposed strategy is able to recover the scene radiance without unwanted color-shifting, and proves that our approach is competitive compared to recent techniques in terms of restored image quality.

We introduce an effective technique to enhance the images captured underwater and degraded due to the medium scattering and absorption. Our method is a single image approach that does not require specialized hardware or knowledge about the underwater conditions or scene structure. It builds on the blending of two images that are directly derived from a color-compensated and white-balanced version of the original degraded image. The two images to fusion, as well as their associated weight maps, are defined to promote the transfer of edges and color contrast to the output image. To avoid that the sharp weight map transitions create artifacts in the low frequency components of the reconstructed image, we also adapt a multiscale fusion strategy. Our extensive qualitative and quantitative evaluation reveals that our enhanced images and videos are characterized by better exposedness of the dark regions, improved global contrast, and edges sharpness. Our validation also proves that our algorithm is reasonably independent of the camera settings, and improves the accuracy of several image processing applications, such as image segmentation and keypoint matching.

Imaging in the underwater environment suffers from color degradation and poor visibility since the light spectrum is selectively absorbed and scattered by water and floating particles. In this paper we introduce a simple but effective underwater dehazing approach that builds on an original color transfer strategy to align the color statistics of a hazy input to the ones of a reference image, also captured underwater, but with neglectable water attenuation. As an original specificity, our proposed color transfer approach is designed to promote the preservation of salient regions, as well as of the details obtained by subtracting from the input an edge-preserving smoothed version of itself. The color-transferred input is then restored by inverting a simplified version of the McGlamery underwater image formation model, using the conventional Dark Channel Prior to estimate the transmission map and the back-scattered light parameter involved in the model. We demonstrate that our color transfer step is crucial for a good transmission estimation but mostly for underwater dehazing where other specialized techniques fail. Extensive qualitative and quantitative results demonstrate the effectiveness of the proposed approach to estimate the transmission, including for cases where traditional specialized techniques fail, and to improve the image quality.

Due to its robustness and effectiveness, multi-scale fusion (MSF) based on the Laplacian pyramid decomposition has emerged as a popular technique that has shown utility in many applications. Guided by several intuitive measures (weight maps) the MSF process is versatile and straightforward to be implemented. However, the number of pyramid levels increases with the image size, which implies sophisticated data management and memory accesses, as well as additional computations. Here, we introduce a simplified formulation that reduces MSF to only a single level process. Starting from the MSF decomposition, we explain both mathematically and intuitively (visually) a way to simplify the classical MSF approach with minimal loss of information. The resulting single-scale fusion (SSF) solution is a close approximation of the MSF process that eliminates important redundant computations. It also provides insights regarding why MSF is so effective. While our simplified expression is derived in the context of high dynamic range imaging, we show its generality on several well-known fusion-based applications, such as image compositing, extended depth of field, medical imaging, and blending thermal (infrared) images with visible light. Besides visual validation, quantitative evaluations demonstrate that our SSF strategy is able to yield results that are highly competitive with traditional MSF approaches.

In this paper we introduce a novel decolorization strategy built on image fusion principles. Decolorization (color-to-grayscale), is an important transformation used in many monochrome image processing applications. We demonstrate that aside from color spatial distribution, local information plays an important role in maintaining the discriminability of the image conversion. Our strategy blends the three color channels R, G, B guided by two weight maps that filter the local transitions and measure the dominant values of the regions using the Laplacian information. In order to minimize artifacts introduced by the weight maps, our fusion approach is designed in a multi-scale fashion, using a Laplacian pyramid decomposition. Additionally, compared with most of the existing techniques our straightforward technique has the advantage to be computationally effective. We demonstrate that our technique is temporal coherent being suitable to decolorize videos. A comprehensive qualitative and also quantitative evaluation based on an objective visual descriptor demonstrates the utility of our decolorization technique.

Dehazing is an image enhancing technique that emerged in the recent years. Despite of its importance there is no dataset to quantitatively evaluate such techniques. In this paper we introduce a dataset that contains 1400+ pairs of images with ground truth reference images and hazy images of the same scene. Since due to the variation of illumination conditions recording such images is not feasible, we built a dataset by synthesizing haze in real images of complex scenes. Our dataset, called D-HAZY, is built on the Middelbury [1] and NYU Depth [2] datasets that provide images of various scenes and their corresponding depth maps. Due to the fact that in a hazy medium the scene radiance is attenuated with the distance, based on the depth information and using the physical model of a hazy medium we are able to create a corresponding hazy scene with high fidelity. Finally, using D-HAZY dataset, we perform a comprehensive quantitative evaluation of several state of the art single-image dehazing techniques.

We introduce an effective technique to enhance night-time hazy scenes. Our technique builds on multi-scale fusion approach that use several inputs derived from the original image. Inspired by the dark-channel [1] we estimate night-time haze computing the airlight component on image patch and not on the entire image. We do this since under night-time conditions, the lighting generally arises from multiple artificial sources, and is thus intrinsically non-uniform. Selecting the size of the patches is non-trivial, since small patches are desirable to achieve fine spatial adaptation to the atmospheric light, this might also induce poor light estimates and reduced chance of capturing hazy pixels. For this reason, we deploy multiple patch sizes, each generating one input to a multiscale fusion process. Moreover, to reduce the glowing effect and emphasize the finest details, we derive a third input. For each input, a set of weight maps are derived so as to assign higher weights to regions of high contrast, high saliency and small saturation. Finally the derived inputs and the normalized weight maps are blended in a multi-scale fashion using a Laplacian pyramid decomposition. The experimental results demonstrate the effectiveness of our approach compared with recent techniques both in terms of computational efficiency and quality of the outputs.

Underwater images suffer from severe perceptual/visual degradation, due to the dense and non-uniform medium, causing scattering and attenuation of the propagated light that is sensed. Typical restoration methods rely on the popular Dark Channel Prior to estimate the light attenuation factor, and subtract the back-scattered light influence to invert the underwater imaging model. However, as a consequence of using approximate and global estimates of the back-scattered light, most existing single-image underwater descattering techniques perform poorly when restoring non-uniformly illuminated scenes. To mitigate this problem, we introduce a novel approach that estimates the back-scattered light locally, based on the observation of a neighborhood around the pixel of interest. To circumvent issue related to selection of the neighborhood size, we propose to fuse the images obtained over both small and large neighborhoods, each capturing distinct features from the input image. In addition, the Laplacian of the original image is provided as a third input to the fusion process, to enhance texture details in the reconstructed image. These three derived inputs are seamlessly blended via a multi-scale fusion approach, using saliency, contrast, and saturation metrics to weight each input. We perform an extensive qualitative and quantitative evaluation against several specialized techniques. In addition to its simplicity, our method outperforms the previous art on extreme underwater cases of artificial ambient illumination and high water turbidity.

In this letter we present a novel strategy to enhance images degraded by the atmospheric phenomenon of haze. Our single-based image technique does not require any geometrical information or user interaction enhancing such images by restoring the contrast of the degraded images. The degradation of the finest details and gradients is constrained to a minimum level. Using a simple formulation that is derived from the lightness predictor our contrast enhancement technique restores lost discontinuities only in regions that insufficiently represent original chromatic contrast of the scene. The parameters of our simple formulation are optimized to preserve the original color spatial distribution and the local contrast. We demonstrate that our dehazing technique is suitable for the challenging problem of image matching based on local feature points. Moreover, we are the first that present an image matching evaluation performed for hazy images. Extensive experiments demonstrates the utility of the novel technique.

Haze is an atmospheric phenomenon that significantly degrades the visibility of outdoor scenes. This is mainly due to the atmosphere particles that absorb and scatter the light. This paper introduces a novel single image approach that enhances the visibility of such degraded images. Our method is a fusion-based strategy that derives from two original hazy image inputs by applying a white balance and a contrast enhancing procedure. To blend effectively the information of the derived inputs to preserve the regions with good visibility, we filter their important features by computing three measures (weight maps): luminance, chromaticity, and saliency. To minimize artifacts introduced by the weight maps, our approach is designed in a multiscale fashion, using a Laplacian pyramid representation. We are the first to demonstrate the utility and effectiveness of a fusion-based technique for dehazing based on a single degraded image. The method performs in a per-pixel fashion, which is straightforward to implement. The experimental results demonstrate that the method yields results comparative to and even better than the more complex state-of-the-art techniques, having the advantage of being appropriate for real-time applications.

This paper describes a novel strategy to enhance underwater videos and images. Built on the fusion principles, our strategy derives the inputs and the weight measures only from the degraded version of the image. In order to overcome the limitations of the underwater medium we define two inputs that represent color corrected and contrast enhanced versions of the original underwater image/frame, but also four weight maps that aim to increase the visibility of the distant objects degraded due to the medium scattering and absorption. Our strategy is a single image approach that does not require specialized hardware or knowledge about the underwater conditions or scene structure. Our fusion framework also supports temporal coherence between adjacent frames by performing an effective edge preserving noise reduction strategy. The enhanced images and videos are characterized by reduced noise level, better exposed-ness of the dark regions, improved global contrast while the finest details and edges are enhanced significantly. In addition, the utility of our enhancing technique is proved for several challenging applications.

This paper introduces an effective decolorization algorithm that preserves the appearance of the original color image. Guided by the original saliency, the method blends the luminance and the chrominance information in order to conserve the initial color disparity while enhancing the chromatic contrast. As a result, our straightforward fusing strategy generates a new spatial distribution that discriminates better the illuminated areas and color features. Since we do not employ quantization or a per-pixel optimization (computationally expensive), the algorithm has a linear runtime, and depending on the image resolution it could be used in real-time applications. Extensive experiments and a comprehensive evaluation against existing state-of-the-art methods demonstrate the potential of our grayscale operator. Furthermore, since the method accurately preserves the finest details while enhancing the chromatic contrast, the utility and versatility of our operator have been proved for several other challenging applications such as video decolorization, detail enhancement, single image dehazing and segmentation under different illuminants.

In this paper we introduce a novel strategy that effectively enhance the visibility of underwater images. Our method is build-up on the fusion strategy that takes a sequence of inputs derived from the initial image. Practically, our fusion-based method aims to yield a final image that overcomes the deficiencies existing in the degraded input images by employing several weight maps that discriminate the regions characterized by poor visibility. The extensive experiments demonstrate the utility of our solution since the visibility range of the underwater images is significantly increased by improving both the scene contrast and the color appearance.

Depth information opens up a lot of possibilities for meaningful editing of photographs. So far, it has only been possible to acquire depth information by either using additional hardware, restrictive scene assumptions or extensive manual input. We developed a novel user-assisted technique for creating adequate depth maps with an intuitive stroke-based user interface. Starting from absolute depth constraints as well as surface normal constraints, we optimize for a feasible depth map over the image. We introduce a suitable smoothness constraint that respects image edges and accounts for slanted surfaces. We illustrate the usefulness of our technique by several applications such as depth of field reduction and advanced compositing.

This paper introduces a technique that increases the spatial resolution of a given video. The method is built on the fundamentals of super-resolution techniques that aim to reconstruct high-resolution frames from a low-resolution input sequence. Different than classical super-resolution methods, besides using the information of adjacent frames, we take advantage of several reference high quality (resolution) photographs of the same scene. The method is purely imagebased, and does not require depth estimation. The additional information extracted from the reference photographs is used to construct several high resolution seed frames added with a constant step in the initial video sequence. Therefore, the seed frames but also the adjacent low-resolution frames provide important information to define priors that are considered in the probabilistic interpretation of the generative model. The estimated solution is obtained based on a standard maximum a posteriori (MAP) approach. Objective tests on real and synthetic video sequences demonstrate the utility and the benefits of the proposed technique over related methods.

Matching images taken from widely different viewpoints is still an open problem being extremely challenging. In this paper is presented a new method that manipulates effectively the color in order to improve the matching performances of the well-known SIFT operator for the wide-baseline case. Without exploiting additional information, the algorithm employs a new model that preserves efficiently the initial variations of the color and lightness. Instead of extracting feature points in the standard gray space, we take advantage of the redistributed chromatic information to increase the distinctness of the neighbor regions of the keypoints. No assumptions are made about the geometry or chromatic content of the initial images. The extensive experiments prove the utility and robustness of the new scheme that is able to find a considerable number of additional valid matches.

Even though the color contains important distinctive information, it is mostly neglected in many fundamental vision applications (such as image matching) since the standard grayscale conversion (luminance channel) is employed extensively. This paper introduces a novel image decolorization technique that besides performing a perceptually accurate color mapping as the other state-of-the-art operators did, it focuses to increase as well the local contrast by manipulating effectively the chromatic information. Additionally, we perform an extensive evaluation of the several recent SIFT-derived local operators in context of image matching when the camera viewpoint is varied and images are differently decolorized based on several recent grayscale operators. The experiments prove the effectiveness of our approach that is able to decolorize accurately images but also to improve the matching results.

This papers introduces a new single image dehazing approach. The method employs a fusion-based strategy that takes as inputs two adapted versions of the original image that are weighted by speci??c maps in order to yield accurate hazefree results. The method computes in a per-pixel fashion being straightforward to be implemented. Our effective method demonstrates to yield comparative and even better results than the more complex state-of-the-art techniques but has the advantage to be appropriate for real-time applications.

In outdoor environments, light reflected from object surfaces is commonly scattered due to the impurities of the aerosol, or the presence of atmospheric phenomena such as fog and haze. Aside from scattering, the absorption coefficient presents another important factor that attenuates the reflected light of distant objects reaching the camera lens. As a result, images taken in bad weather conditions (or similarly, underwater and aerial photographs) are characterized by poor contrast, lower saturation and additional noise.

Recent efforts to accurately integrate properties of human visual perception have fueled an impressive improvement not only in digital photography but also in color manipulation tools. Color is one of the main attributes that influences human visual system, being ultimately the result of our experience and perception. However, several important applications such as substitution systems for visually impaired people, computerized tomography, printed textbooks and aesthetical stylization still require a reliable grayscale representation of images. Mapping three dimensional color information onto a single dimension while still preserving the original appearance, contrast and finest details is not a trivial task. Standard monochromatic transformations, found in commercial image editing software, neglect the color distribution, being unable to conserve in many cases the discriminability of the original chromatic contrast. Consequently, isoluminant colors are mapped to similar grayscale levels increasing the ambiguity in the decolorized image version. Although luminance is a definite value, our subjective interpretation that measures apparent brightness is very important in image decolorization process.

Abstract Restoration of the photographs damaged by the camera shake is a challenging task that manifested increasing attention in the recent period. Despite of the important progress of the blind deconvolution techniques, due to the ill‐posed nature of the problem, the finest details of the kernel blur cannot be recovered entirely. Moreover, the additional constraints and prior assumptions make these approaches to be relative limited. In this paper we introduce a novel technique that removes the undesired blur artifacts from photographs taken by hand‐held digital cameras. Our approach is based on the observation that in general several consecutive photographs taken by the users share image regions that project the same scene content. Therefore, we took advantage of additional sharp photographs of the same scene. Based on several invariant local feature points, filtered from the given blurred/non‐blurred images, our approach matches the keypoints and estimates the blur kernel using additional statistical constraints. We also present a simple deconvolution technique that preserves edges while minimizing the ringing artifacts in the restored latent image. The experimental results prove that our technique is able to infer accurately the blur kernel while reducing significantly the artifacts of the spoilt images.