Video coding for machines (VCM) is an emerging approach in video compression designed to optimize content for machine analysis tasks. Although VCM was initially developed for machine vision, scalable coding frameworks have been developed to support both machine-driven analysis and human viewing as required. In this work, we focus on scenarios where high-quality encoding of regions of interest (ROIs) for machine vision and low-bitrate encoding of the background (BG) for human vision. At the decoder, severely degraded BG quality in reconstructed frames makes them unsuitable for viewing; therefore, restoring the degraded BGs by leveraging high-quality ROIs is essential. To this end, we propose the Gradient-Guided Diffusion Restoration (GGDR) algorithm, which integrates a pretrained generative diffusion model with content-aware supervision and adaptive refinement mechanisms to restore severely degraded regions robustly while maintaining visual consistency across the entire frame. The GGDR algorithm consists of two key components: (i) a content-aware supervision mechanism that preserves salient features and structural information in the input image, ensuring superior performance even with challenging high-variance inputs and (ii) a refinement block that guides the generation process of the pretrained diffusion model based on a degradation model and structural guidance. Experimental results demonstrate that the proposed algorithm outperforms state-of-the-art algorithms both qualitatively and quantitatively.

We propose content-aware supervision (CAS) techniques for diffusion-based restoration of an extremely compressed background for video coding for machines (VCM). First, we develop a CAS block to exploit prior information in an input image to reconstruct the noisy image, which is used as the input for the pretrained diffusion model. Then, we construct a refinement block to guide the pretrained diffusion model at each diffusion step by incorporating a degradation model and correction gradient estimation. Experimental results demonstrate the proposed algorithm outperforms state-of-the-art algorithms.

We propose an infrared and visible image fusion algorithm using modality-shared and modality-specific feature decomposition transformers. First, the proposed algorithm extracts multiscale shallow features of infrared and visible images. Then, we develop modality-shared and modality-specific feature decomposition transformers that decompose the features into common and complementary components for each modality. For better decomposition, we develop a decomposition loss by constraining the common features to be correlated while the complementary features are uncorrelated. Finally, the reconstruction block generates the fused image by combining the common and complementary features. Experimental results show that the proposed algorithm significantly outperforms conventional algorithms on several datasets.

Image fusion techniques aim to generate more informative images by merging multiple images of different modalities with complementary information. Despite significant fusion performance improvements of recent learning-based approaches, most fusion algorithms have been developed based on convolutional neural networks (CNNs), which stack deep layers to obtain a large receptive field for feature extraction. However, important details and contexts of the source images may be lost through a series of convolution layers. In this work, we propose a cross-modal transformer-based fusion (CMTFusion) algorithm for infrared and visible image fusion that captures global interactions by faithfully extracting complementary information from source images. Specifically, we first extract the multiscale feature maps of infrared and visible images. Then, we develop cross-modal transformers (CMTs) to retain complementary information in the source images by removing redundancies in both the spatial and channel domains. To this end, we design a gated bottleneck that integrates cross-domain interaction to consider the characteristics of the source images. Finally, a fusion result is obtained by exploiting spatial-channel information in refined feature maps using a fusion block. Experimental results on multiple datasets demonstrate that the proposed algorithm provides better fusion performance than state-of-the-art infrared and visible image fusion algorithms, both quantitatively and qualitatively. Furthermore, we show that the proposed algorithm can be used to improve the performance of computer vision tasks, e.g., object detection and monocular depth estimation.

We propose a learning-based image restoration algorithm for a single decoded image with a high-quality foreground and an extremely degraded background for video coding for machines (VCM). First, we develop an encoder that extracts multiscale features and learns latent vectors. Then, a background generator with style and feature fusion blocks generates guided features that contain the prior background information in the input image. Finally, the decoder restores the degraded background region by merging the image features from the encoder and prior background information from the generator. Experimental results show that the proposed algorithm achieves better performance than state-of-the-art algorithms.

Most deep learning-based image enhancement algorithms have been developed based on the image-to-image translation approach, in which enhancement processes are difficult to interpret. In this paper, we propose a novel interpretable image enhancement algorithm that estimates multiple transformation functions to describe complex color mapping. First, we develop a histogram-based multiple transformation function estimation network (HMTF-Net) to estimate multiple transformation functions by exploiting both the spatial and statistical information of the input images. Second, we estimate pixel-wise weight maps, which indicate the contribution of each transformation function at each pixel, based on the local structures of the input image and the transformed images obtained by each transformation function. Finally, we obtain the enhanced image as the weighted sum of the transformed images using the estimated weight maps. Extensive experiments confirm the effectiveness of the proposed approach and demonstrate that the proposed algorithm outperforms state-of-the-art image enhancement algorithms for different image enhancement tasks.

We propose an infrared and visible image fusion algorithm using bimodal transformers. First, the proposed algorithm extracts multiscale features of the input infrared and visible images. Then, we develop the bimodal transformers that refine the extracted features by estimating their irrelevance maps to exploit the complementary information of the source images. Finally, we develop a reconstruction block that generates the fusion result by merging the refined features in the frequency domain to exploit the global information of the source images. Experimental results show that the proposed algorithm outperforms state-of-the-art infrared and visible image fusion algorithms on several datasets.

We propose a learning-based low-light image enhancement algorithm, called the histogram-based transformation function estimation network (HTFNet), that estimates transformation functions using the histogram of an input image. First, we obtain an attention image that indicates the pixel-wise information on the level of enhancement. Then, the proposed HTFNet generates the transformation functions by exploiting both the spatial and statistical information of the input image by combining two feature maps extracted from the input image and its histogram. Finally, the enhanced images are obtained via channel-wise intensity transformation. Experimental results show that the proposed algorithm provides higher image quality compared with the state-of-the-art algorithms.