Exploring and Evaluating Image Restoration Potential in Dynamic Scenes
Cheng Zhang, Shaolin Su, Yu Zhu, Qingsen Yan, Jinqiu Sun, Yanning, Zhang

TL;DR
This paper introduces the concept of image restoration potential (IRP) in dynamic scenes, proposing a dataset and a deep model to predict IRP, which can enhance image restoration, frame selection, and camera setting optimization.
Contribution
The paper presents the first study on IRP, including a new dataset and a deep learning model for accurate IRP prediction, enabling improved restoration and camera parameter tuning.
Findings
IRP can be effectively predicted using the proposed deep model.
IRP prediction benefits frame filtering and restoration quality.
IRP serves as a useful indicator for camera setting optimization.
Abstract
In dynamic scenes, images often suffer from dynamic blur due to superposition of motions or low signal-noise ratio resulted from quick shutter speed when avoiding motions. Recovering sharp and clean results from the captured images heavily depends on the ability of restoration methods and the quality of the input. Although existing research on image restoration focuses on developing models for obtaining better restored results, fewer have studied to evaluate how and which input image leads to superior restored quality. In this paper, to better study an image's potential value that can be explored for restoration, we propose a novel concept, referring to image restoration potential (IRP). Specifically, We first establish a dynamic scene imaging dataset containing composite distortions and applied image restoration processes to validate the rationality of the existence to IRP. Based on…
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Taxonomy
TopicsAdvanced Image Processing Techniques · Image Processing Techniques and Applications · Image and Signal Denoising Methods
MethodsSPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings
