Enhancing a low-light noisy RAW image into a well-exposed and clean sRGB image is a significant challenge for modern digital cameras. Prior approaches have difficulties in recovering fine-grained details and true colors of the scene under extremely low-light environments due to near-to-zero SNR. Meanwhile, diffusion models have shown significant progress towards general domain image generation. In this paper, we propose to leverage the pre-trained latent diffusion model to perform the neural ISP for enhancing extremely low-light images. Specifically, to tailor the pre-trained latent diffusion model to operate on the RAW domain, we train a set of lightweight taming modules to inject the RAW information into the diffusion denoising process via modulating the intermediate features of UNet. We further observe different roles of UNet denoising and decoder reconstruction in the latent diffusion model, which inspires us to decompose the lowlight image enhancement task into latent-space low-frequency content generation and decoding-phase high-frequency detail maintenance. Through extensive experiments on representative datasets, we demonstrate our simple design not only achieves state-of-the-art performance in quantitative evaluations but also shows significant superiority in visual comparisons over strong baselines, which highlight the effectiveness of powerful generative priors for neural ISP under extremely low-light environments.

High-quality portrait photography has become an essential function in our daily lives. However, due to the limited aperture and focal length of a smartphone camera, images captured by a smartphone cannot match the same level of bokeh effect by a digital single-lens reflex camera. A typical solution on a smartphone is to simulate out-of-focus effects from an all-in-focus image, where the key is robust depth estimation and portrait matting. To achieve this, we propose a multi-stage, multi-branch matting network to estimate a strand-level portrait alpha mask, which is then used to refine the coarse depth map obtained from the pre-trained model. Combining the input portrait image with the estimated depth map and alpha mask, we propose a learning-free optimization mechanism to construct a multi-plane image (MPI) representation for depth-of-field synthesis. The MPI consists of multiple layers of disk-blurred images with kernel size proportional to the absolute depth distance to the focus layer. Then a depth-aware blurring process is applied to enforce the bokeh effect. Besides, each MPI layer has an alpha channel controlling the visibility according to the corresponding depth. Finally, an image with bokeh is rendered by compositing all MPI layers. We conduct comprehensive experiments to evaluate our method, which demonstrates that our method can generate more accurate alpha masks and more realistic images with bokeh compared to prior work.

Abstract Considerable progress has been made in high dynamic range (HDR) image reconstruction from multi-exposure low dynamic range (LDR) frames in recent years. Despite achieving satisfactory HDR results while handling the misalignment among LDR frames, previous studies pay less attention to a prevalent but more crucial concern: the significant noise and motion blur that exist in multi-exposure frames captured by a handheld camera. To overcome these extensive and challenging corruptions, we achieve the HDR imaging task from two key aspects: First, due to the absence of related datasets, previous learning-based methods struggle with performing high-quality HDR imaging when the input LDR frames are confronted with real-world image noise and motion blur. Recognizing the importance of this aspect, we propose the first available realistic dataset based on the real-world burst imaging pipeline for training and evaluating different methods in the joint HDR imaging, denoising, and deblurring task. Second, due to the corruption-insensitive of previous network architectures, we propose a novel and efficient attention-based multi-exposure HDR imaging method, which skillfully selects the optimal information (clean or sharp) from corrupted LDR inputs by our customized cross-attention mechanism to generate HDR information. Furthermore, to enhance the robustness of our cross-attention mechanism, we introduce a novel E ntropy D ecreasing loss (ED loss) which decreases the entropy of the calculated attention map to alleviate the ghosting artifacts during multi-exposure information fusion. Extensive experimental results demonstrate that the proposed method trained on the proposed dataset surpasses related state-of-the-art methods with outstanding real-world photography quality. Codes and the dataset will be available.

Solving the task of inverse imaging problems can restore unknown clean images from input measurements that have incomplete information. Utilizing powerful generative models, such as denoising diffusion models, could better tackle the ill-posed issues of inverse problems with the distribution prior of the unknown clean images. We propose a learnable state-estimator-based diffusion model to incorporate the measurements into the reconstruction process. Our method makes efficient use of the pre-trained diffusion models with computational feasibility compared to the conditional diffusion models, which need to be trained from scratch. In addition, our pipeline does not require explicit knowledge of the image degradation operator or make the assumption of its form, unlike many other works that use the pre-trained diffusion models at the test time. The experiments on three typical inverse imaging problems (both linear and non-linear), inpainting, deblurring, and JPEG compression restoration, have comparable results with the state-of-the-art methods.

We present ENTED, a new framework for blind face restoration that aims to restore high-quality and realistic portrait images. Our method involves repairing a single degraded input image using a high-quality reference image. We utilize a texture extraction and distribution framework to transfer high-quality texture features between the degraded input and reference image. However, the StyleGAN-like architecture in our framework requires high-quality latent codes to generate realistic images. The latent code extracted from the degraded input image often contains corrupted features, making it difficult to align the semantic information from the input with the high-quality textures from the reference. To overcome this challenge, we employ two special techniques. The first technique, inspired by vector quantization, replaces corrupted semantic features with high-quality code words. The second technique generates style codes that carry photorealistic texture information from a more informative latent space developed using the high-quality features in the reference image’s manifold. Extensive experiments conducted on synthetic and real-world datasets demonstrate that our method produces results with more realistic contextual details and outperforms state-of-the-art methods. A thorough ablation study confirms the effectiveness of each proposed module.

Since the advent of social media platforms, image selection based on social preference is a challenging task that all users inherently undertake before sharing images with the public. In our user study for this problem, human choices of images based on perceived social preference are largely inaccurate (58.7% accuracy). The challenge of this task, also known as image popularity assessment, lies in its subjective nature caused by visual and non-visual factors. Especially in the social media setting, social feedback on a particular image largely differs depending on who uploads it. Therefore social preference model should be able to account for this user-specific image aspect of the task. To address this issue, we present a retrieval-augmented approach that leverages both image features and user-specific statistics for neural image popularity assessment. User-specific statistics are derived by retrieving past images with their statistics from a memory bank. By combining these statistics with image features, our approach achieves 79.5% accuracy, which significantly outperforms human and baseline models on the pairwise ranking of images from the Instagram Influencer Dataset. Our source code will be publicly available.

Compared to traditional learning from scratch, knowledge distillation sometimes makes the DNN achieve superior performance. In this paper, we provide a new perspective to explain the success of knowledge distillation based on the information theory, i.e., quantifying knowledge points encoded in intermediate layers of a DNN for classification. To this end, we consider the signal processing in a DNN as a layer-wise process of discarding information. A knowledge point is referred to as an input unit, the information of which is discarded much less than that of other input units. Thus, we propose three hypotheses for knowledge distillation based on the quantification of knowledge points. 1. The DNN learning from knowledge distillation encodes more knowledge points than the DNN learning from scratch. 2. Knowledge distillation makes the DNN more likely to learn different knowledge points simultaneously. In comparison, the DNN learning from scratch tends to encode various knowledge points sequentially. 3. The DNN learning from knowledge distillation is often more stably optimized than the DNN learning from scratch. To verify the above hypotheses, we design three types of metrics with annotations of foreground objects to analyze feature representations of the DNN, i.e., the quantity and the quality of knowledge points, the learning speed of different knowledge points, and the stability of optimization directions. In experiments, we diagnosed various DNNs on different classification tasks, including image classification, 3D point cloud classification, binary sentiment classification, and question answering, which verified the above hypotheses.

This paper presents a method to interpret the success of knowledge distillation by quantifying and analyzing task-relevant and task-irrelevant visual concepts that are encoded in intermediate layers of a deep neural network (DNN). More specifically, three hypotheses are proposed as follows. 1. Knowledge distillation makes the DNN learn more visual concepts than learning from raw data. 2. Knowledge distillation ensures that the DNN is prone to learning various visual concepts simultaneously. Whereas, in the scenario of learning from raw data, the DNN learns visual concepts sequentially. 3. Knowledge distillation yields more stable optimization directions than learning from raw data. Accordingly, we design three types of mathematical metrics to evaluate feature representations of the DNN. In experiments, we diagnosed various DNNs, and above hypotheses were verified.