Adaptive gamma processing of the video cameras for the expansion of the dynamic range

Sakaue, S.; Nakayama, M.; Tamura, Akihiro; Maruno, S.

doi:10.1109/30.468094

Cited by 44 publications

(12 citation statements)

References 7 publications

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“…Without tone mapping, these signals are not visible on a standard monitor due to saturation effect [27]. Adaptive methods ensure local contrast enhancement using local gamma [25], local histogram [32] or Retinex-like [19] approaches.…”

Section: Contrast Enhancementmentioning

confidence: 98%

The eISP low-power and tiny silicon footprint programmable video architecture

Thevennin

Paindavoine

Letellier

et al. 2010

J Real-Time Image Proc

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International audienceCMOS sensors are now more and more frequently integrated into popular consumer products. Images from these sensors thus need to be digitally processed for display purposes. To do so, CMOS sensors are associated with dedicated components that keep power consumption low. However, use of dedicated components limits hardware flexibility and prevents updating of image processing algorithms. This paper describes the eISP, a programmable processing architecture that combines enough computational efficiency for 1080p HD video with silicon area and power characteristics suitable for the next generation of mobile phones (lower than 1 mm2 and 500 mW in TSMC 65 nm)

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Section: Contrast Enhancementmentioning

confidence: 98%

The eISP low-power and tiny silicon footprint programmable video architecture

Thevennin

Paindavoine

Letellier

et al. 2010

J Real-Time Image Proc

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“…Contrast enhancement techniques can be used to improve backlight images. There are many contrast enhancement algorithms [5][6][7][8][9][10][11], and among these, histogram equalization [5][6][7][8][9] is the most popular automatic method due to its simplicity and effectiveness. Global histogram equalization uses the histogram information of an image as its transformation function that stretches the contrast of the high histogram region and compresses the contrast of the low histogram region.…”

Section: Introductionmentioning

confidence: 99%

A Neuro-Fuzzy Approach for Compensating Color Backlight Images

Yang

Chen

2006

Neural Process Lett

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This paper presents a neuro-fuzzy approach for compensating exposure in the case of backlighting, regardless of the position of objects. To achieve the compensation effect, the fuzzy C-means algorithm is first used to extract features from a backlight image. Then these extracted features are presented to a trained artificial immune system based neuro-fuzzy system (AISNFS) to estimate the amount of compensation. Finally, the estimated amount of compensation incorporated with a compensation equation is used to enhance the intensity component of the backlight image to produce a compensated image. Several backlight images were used to test the performance of the algorithm.

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“…In our previous work [2], tonal mapping was performed directly on the complementary mosaic sensor signal which creates a difficult problem of non-linear color changes: preservation of saturation and hue of the colors. Fuzzy logic [3] as well as variable transfer functions in combination with the local contrast enhancement [4] were successfully used, but only up to 10-dB expansion of the dynamic range was achieved. The combination of image fusion and tone reproduction as proposed in [5] looks promising for static digital images, but cannot work well with digital video cameras where large object or camera motion is involved and no recording delays are allowed.…”

Section: Introductionmentioning

confidence: 99%