A Hierarchical Approach for Rain or Snow Removing in A Single Color Image

Abstract

In this paper, we propose an efficient algorithm to remove rain or snow from a single color image. Our algorithm takes advantage of two popular techniques employed in image processing, namely, image decomposition and dictionary learning. At first, a combination of rain/snow detection and a guided filter is used to decompose the input image into a complementary pair: (1) the low-frequency part that is free of rain or snow almost completely and (2) the high-frequency part that contains not only the rain/snow component but also some or even many details of the image. Then, we focus on the extraction of image’s details from the high-frequency part. To this end, we design a 3-layer hierarchical scheme. In the first layer, an over-complete dictionary is trained and three classifications are carried out to classify the high-frequency part into rain/snow and nonrain/snow components in which some common characteristics of rain/snow have been utilized. In the second layer, another combination of rain/snow detection and guided filtering is performed on the rain/snow component obtained in the first layer. In the third layer, the sensitivity of variance across color channels (SVCC) is computed to enhance the visual quality of rain/snow-removed image. The effectiveness of our algorithm is verified through both subjective (the visual quality) and objective (through rendering rain/snow on some ground-truth images) approaches, which shows a superiority over several state-of-the-art works.

Existing System

Image Decomposition, Sparse Coding.

Proposed System

In our work, we consider the rain/snow removal from a single color image, in which several new designs are introduced. The main contributions of our work are summarized as follows: We have outlined several common characteristics of rain and snow, from which two metrics are defined, namely, the sensitivity of variance across color channels (SVCC) and the principal direction of an image patch (PDIP). A low-frequency part that is free of rain or snow almost completely has been generated, thanks to the use of a combination of rain/snow detection and a guided filter (as the low-pass filter), while the corresponding high-frequency part is made complementary to the low frequency part. A 3-layer hierarchy of extracting image’s details from the high-frequency part has been designed. Specifically, the first layer is a 3-times classification that is based on a trained dictionary (over-complete), the second layer applies another combination of rain/snow detection and a guided filter, and the third layer utilizes the SVCC to enhance the visual quality of the rain/snow-removed image.

Architecture

BLOCK DIAGRAM