對(duì)比總結(jié)

1专肪、09年的通過保邊濾波解決 halo artifacts （halo artifacts are present at sharp edges where the luminance of the NIR and visible image are significantly different.）但是在亮度差異較大的區(qū)域您炉，色彩失真比較大妒御。（As we are only working with the luminance channel, colors might appear unrealistic in cases of extreme luminance changes 溉苛。
2鲤孵、Feng et al.'s method fuses the RGB and NIR images based on transmission;
通過nir和RGB的不同求得transmission。但是這種方法的Feng etal.'s method creates ghost artefacts around cloud舍沙。
3.Dong 避免了 ghost artefacts around cloud朵栖，但是颊亮，由于仍采用了dark priority的方法，使得圖像的顏色加重陨溅，不自然终惑，看不清細(xì)節(jié)。視覺上提升效果不大声登。
4.Chang-Hwan Son的先給nir做color mapping狠鸳，但是如果nir和RGB有slightly misaligned，或者 the luminance of the NIR and visible image are significantly different悯嗓，就會(huì)有halo artifacts和color distortion件舵。

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chang-hwan的

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1. 《COLOR IMAGE DEHAZING USING THE NEAR-INFRARED》Lex Schaul
   突破點(diǎn)：the halo artifacts that disappear by using edge-preserving filters.However, halo artifacts are present at sharp edges where the luminance of the NIR and visible image are significantly different. By making use of the WLS filters, these artifacts disappear.
   不足：As we are only working with the luminance channel, colors might appear unrealistic in cases of extreme luminance changes (Fig.3). We want to investigate in the future how our images can be improved by additional color processing.

   
   
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2. 《NEAR-INFRARED GUIDED COLOR IMAGE DEHAZING》Chen Feng
   Image dehazing in general involves two tasks, removing the airlight color effect and recovering the lost details.Removing the airlight color is fundamental to image dehazing. Inaccurate estimation of the airlight color could result in unwanted color shift
   in two-fold:
   Design an optimization framework to resolve the image de-hazing problem guided with NIR gradient constraints.
   Refine airlight color estimation by exploiting the differences between NIR and RGB channels.
   估計(jì)的大氣光 都是一個(gè)固定值，但是大氣是分布的桶蝎，局部和全局是有差異的驻仅，并且對(duì)于t,在每個(gè)通道的t也是不同的。

3. 《Colour image dehazing using near-infrared fusion》Dong-Won Jang, Rae-Hong Park
   Schaul et al.'s method distorts the colour of the input image by fusing the NIR image. Although Feng et al.'s method removes the haze efficiently, some significant artefacts usually appear at cloud boundaries that are not aligned with those of the NIR image with small transmission values.
   Regions or images without haze remain unaltered. Therefore, our method can be applied whether haze is actually present or not.
   Both methods efficiently remove haze in the mountainous area beyond the stretch of water. However, the input images are slightly misaligned, and Feng et al.'s method fuses the RGB and NIR images based on transmission; as expected, Feng etal.'s method creates ghost artefacts around cloud, as shown in Fig. 6c. In contrast, the proposed method fuses images based on the HF distribution of the local patch and so, as shown in Fig. 6d, no ghost artefact is observed in misaligned regions.

   
   
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   4.There are serious discrepancies in terms of brightness and image structures between the near-infrared image and the visible color image. Due to this discrepancy, the direct use of the near-infrared image for haze removal causes a color distortion problem during near-infrared fusion. The key objective for the near-infrared fusion is therefore to remove the color distortion as well as the haze.
   In short, the new issue for the near-infrared fusion is to preserve high visibility of the captured near-infrared image and to remove color distortion that appears during image fusion
   加入了顏色和深度的正則化先驗(yàn)知識(shí)登渣，進(jìn)行約束雾家。逼近先驗(yàn)。

考慮到的現(xiàn)象還有實(shí)驗(yàn)的一些發(fā)現(xiàn)

NIR的不同物質(zhì)區(qū)域的反射特性不同绍豁。

物質(zhì)對(duì)近紅外和RGB四通道的反射率差異比較大，通過NVDI指數(shù)牙捉，植被等區(qū)域近紅外強(qiáng)于RGB竹揍，天空霧氣水等近紅外弱于RGB敬飒。就是圖像的對(duì)比度差異比較大。RGB三個(gè)通道和

近紅外圖像不能理解為一個(gè)單純的亮度圖像芬位。

然而由于物體成像根據(jù)Retinx理論分為入射光和反射光无拗，所以，當(dāng)想要求RGB和NIR的反射的差異時(shí)昧碉，先使用了同態(tài)濾波去除了入射光的影響英染。

這個(gè)差異圖，代表了自然場景下被饿，物質(zhì)對(duì)NIR和RGB的反射率的不同四康，是考慮到了物質(zhì)的反射的物理特性。------------------

引導(dǎo)濾波和雙邊濾波的效果類似狭握，但是闪金，速度快。

因?yàn)?NIR和RGB的對(duì)比度是不同的论颅，所以在將detail層融合的時(shí)候哎垦，盡量不引進(jìn)近紅外的對(duì)比度信息，而是主要引進(jìn)的是紋理信息恃疯。而通過商圖像(光照不變的 illumination invariant)漏设，可以最大限度的隱藏對(duì)比度信息，保留紋理信息今妄。

The ratio is computed on each RGB channel separately and is independent of the signal magnitude and surface reflectance. The ratio captures the local detail variation in and is commonly called a quotient image [Shashua and Riklin-Raviv 2001] or ratio image [Liu et al. 2001] in computer vision

We use the detail information from the NIR image to both reduce noise in the RGB image and sharpen its detail. The base layer of RGB image contains low luminance information as perceived by humans visual system, thus the NIR base layer is discarded. 如此郑口，可以保持融合以后顏色的自然。