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Case Studies | Electronics & Communication Engineering | India | Volume 3 Issue 6, June 2014

An Approach for Reduction of Rain Streaks from a Single Image

Vijayakumar Majjagi | Netravati U M

Abstract: Various weather conditions such as rain; snow; haze; or fog will cause complex visual effects of spatial or temporal domains in images or videos. Such effects may significantly degrade the quality of outdoor vision systems. Rain removal from a video is a challenging problem and has been recently investigated extensively. Nevertheless; the problem of rain removal from a single image was rarely studied in the literature; where no temporal information among successive images can be exploited; making the problem very challenging. In this paper; we propose a single-image-based rain removal framework. We first decompose an image into the low-frequency and high-frequency parts using a smoothing (bilateral) filter. Bilateral filter is edge preserving; noise reducing; smoothing filter for images. Bilateral filter extends the concept of Gaussian smoothing by weighting the filter coefficients with their corresponding relative pixel intensities. This weight is based on Gaussian distribution. Pixels that are very different in intensity from the central pixel are weighted less even though they may be in close proximity to the central pixel. After the high-frequency part is then decomposed into rain component and non-rain component by performing dictionary learning and sparse coding. As a result; the rain component can be successfully removed from the image while preserving most original image details. The refined guidance image can be used to get a better removal result. Reduction or removal of rain streaks fall into the category of image noise reduction.

Keywords: Various weather conditions such as rain, snow, haze, or fog will cause complex visual effects of spatial or temporal domains in images or videos Such effects may significantly degrade the quality of outdoor vision systems Rain removal from a video is a challenging problem and has been recently investigated extensively Nevertheless, the problem of rain removal from a single image was rarely studied in the literature, where no temporal information among successive images can be exploited, making the problem very challenging In this paper, we propose a single-image-based rain removal framework We first decompose an image into the low-frequency and high-frequency parts using a smoothing bilateral filter Bilateral filter is edge preserving, noise reducing, smoothing filter for images Bilateral filter extends the concept of Gaussian smoothing by weighting the filter coefficients with their corresponding relative pixel intensities This weight is based on Gaussian distribution Pixels that are very different in intensity from the central pixel are weighted less even though they may be in close proximity to the central pixel After the high-frequency part is then decomposed into rain component and non-rain component by performing dictionary learning and sparse coding As a result, the rain component can be successfully removed from the image while preserving most original image details The refined guidance image can be used to get a better removal result Reduction or removal of rain streaks fall into the category of image noise reduction

Edition: Volume 3 Issue 6, June 2014,

Pages: 973 - 977