Under Display Camera (UDC) is a novel imaging system that mounts a digital camera lens beneath a display panel with the panel covering the camera.

Image restoration tasks are ill-posed problems whose solutions are not unique.

In this supplementary material, we provide additional discussions and results. B, we provide more results on various tasks, i.e., blind face restoration, old photo restoration, During the inference process, there involves hyperparameters belonging to three categories. Parameters for optional quality enhancement ( e.g., the range for multiple gradient steps to take place Table 1: Default hyperparameter settings in our experiments.T ask Sampling Partial Guidance Optional s As shown in Fig.1, when all the other inference settings are the same, we Input faces are corrupted by real-world degradations. Our method produces high-quality faces with faithful details. ( Zoom in for best view) 3 B.2 More Results on Old Photo Restoration This work focuses on restoring images corrupted by various forms of degradations. This could potentially lead to deceptive information, such as incorrect identity recognition.

Image restoration aims to recover clear high-quality images from given degraded ones.

Although image restoration has achieved significant progress, its potential to assist object detectors in adverse imaging conditions lacks enough attention. It is reported that the existing image restoration methods cannot improve the object detector performance and sometimes even reduce the detection performance. To address the issue, we propose a targeted adversarial attack in the restoration procedure to boost object detection performance after restoration. Specifically, we present an ADAM-like adversarial attack to generate pseudo ground truth for restoration training. Resultant restored images are close to original sharp images, and at the same time, lead to better results of object detection. We conduct extensive experiments in image dehazing and low light enhancement and show the superiority of our method over conventional training and other domain adaptation and multi-task methods. The proposed pipeline can be applied to all restoration methods and detectors in both one-and two-stage.

We present a simple and effective approach for non-blind image deblurring, combining classical techniques and deep learning.

Natural images captured by mobile devices often suffer from multiple types of degradation, such as noise, blur, and low light. Traditional image restoration methods require manual selection of specific tasks, algorithms, and execution sequences, which is time-consuming and may yield suboptimal results. All-in-one models, though capable of handling multiple tasks, typically support only a limited range and often produce overly smooth, low-fidelity outcomes due to their broad data distribution fitting. To address these challenges, we first define a new pipeline for restoring images with multiple degradations, and then introduce RestoreAgent, an intelligent image restoration system leveraging multimodal large language models. RestoreAgent autonomously assesses the type and extent of degradation in input images and performs restoration through (1) determining the appropriate restoration tasks, (2) optimizing the task sequence, (3) selecting the most suitable models, and (4) executing the restoration. Experimental results demonstrate the superior performance of RestoreAgent in handling complex degradation, surpassing human experts.