请帮我翻译一下这一段英文吧（400多字）,是专业文献里面的（数字图像处理）有点难度,翻译的好再追加分.

请帮我翻译一下这一段英文吧（400多字）,是专业文献里面的（数字图像处理）有点难度,翻译的好再追加分.
我从来没有翻译过这种论文,自己翻译了一下午才译出了3000字,而且读起来很不顺畅,感觉上还是英文式的中文.我想请教一下专业的朋友,这段到底该怎样翻译,我想仿照您给翻译的风格继续翻译下去.
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Integrating this process along a ray emerging from the
viewer, in the case of a spatially varying β , gives
t=exp(-∫β(r(s))ds), (1)
where r is an arc-length parametrization of the ray. The fraction t is called the transmission and expresses the relative portion of light that managed to survive the entire path between the observer and a surface point in the scene, at r(d), without being scattered. In the absence of black-body radiation the process of light scattering conserves energy, meaning that the fraction of light scattered from any particular direction is replaced by the same fraction of light scattered from all other directions. The equation that expresses this conservation law is known as the Radiative Transport Equation [Rossum and Nieuwenhuizen 1999]. Assuming that this added light is dominated by light that underwent multiple scattering events, allows us to approximate it as being both isotropic and uniform in space. This constant light, known as the airlight [Koschmieder 1924] or also as the veiling light, can be used to approximate the true in-scattering term in the full radiative transport equation to achieve the following simpler image formation model
I(x)=t(x)J(x)+(1-t(x))A, (2)
where this equation is defined on the three RGB color channels. I stands for the observed image, A is the airlight color vector, J is the surface radiance vector at the intersection point of the scene and the real-world ray corresponding to the pixel x = (x;y), and t(x) is the transmission along that ray. This degradation model is commonly used to describe the image formation in the presence of haze [Chavez 1988; Nayar and Narasimhan 1999; Narasimhan and Nayar 2000; Schechner et al. 2001; Narasimhan and Nayar 2003; Shwartz et al. 2006]. Similar to the goal of these work, we are interested here in recovering J which is an image showing the scene through a clear haze-free medium. By that we do not eliminate other effects, the haze may have on the scene, such as a change in overall illumination which in turn affects the radiant emittance. Also, we assume that the input image I is given in the true scene radiance values. These radiance maps can be recovered by extracting the camera raw data or inverting the overall acquisition response curve, as described in [Debevec and Malik 1997]. This model (2) explains the loss of contrasts due to haze as the result of averaging the image with a constant color A. If we measure the contrasts in the image as the magnitude of its gradient field, a scene J seen through a uniform medium with t(x) = t < 1 gives us
|▽I|=|t▽J(x)+(1-t) ▽A|=t|▽J(x)|

我帮你翻译了第二段,你看看可不可以. 第一段挺专业的,我的水平只能看懂还翻译不到那么好...
L(x)=t(x)J(x) + (1-t(x))A (2)
这个公式是在RGB三元色的基础上定义的. 其中的I 代表被观察图像. A是空气光的色彩向量. J是指在景象和对于像素x=(x,y)的真实射线的交点的表面辐射向量. T(x) 是指沿着该射线的透光率.
这个衰减模型一般用于在烟雾中的景象形成. [Chavez 1988; Nayar and Narasimhan 1999; Narasimhan and Nayar 2000; Schechner et al. 2001; Narasimhan and Nayar 2003; Shwartz et al. 2006]. 和他们的工作相似, 我们也在致力于求解J这个变量. 因为J 是一个可以反映景象穿过一个无烟的介质的图像. 但我们不想忽略其它影响因素. 景象上有可能有烟雾的存在. 例如,整体照明强度的改变反过来会影响辐射度. 同时,我们假设输入的图象I 以景象的真实辐射值表达. 这些辐射图谱可以从相机的原始数据中提取,或者像Debevec描述的那样通过总体获取响应曲线来转化得到 [Debevec and Malik 1997]. 这个模型解释了为什么为考虑烟雾的影响而将图像和一个固定的颜色A取平均值会导致的对比度的降低. 如果我们根据图象自身的渐变场的等级来测量图象的对比度. 当一个景象J通过一个均匀的介质 当t(x)=t